Vocational and Career Tech Education in AmericanHigh Schools: Curriculum Choice and Labor Market

Outcomes

Daniel KreismanDepartment of EconomicsGeorgia State University

Kevin StangeFord School of Public Policy

University of Michigan

September, 2015Draft - please do not circulate or cite.

Abstract

We develop a framework for high school curriculum choice in which students learn aboutability and subsequently allocate time between vocational, core and elective courses. Wethen observe how these decisions impact college attendance, completion and subsequentearnings in the NLSY97, with a particular focus on vocational, or career tech, educa-tion. In doing so, we depart from the traditional treatment of vocational coursework asa “track” and treat each additional course as a marginal decision. We find that while vo-cational courses marginally deter four-year college attendance, they have no impact ongraduation. Moreover, we estimate a two percent wage premium from each additionalyear of advanced vocational coursework, but no benefit (or harm) comes from basicvocational courses. We conclude that while vocational education plays an importantrole for non-college graduates, policy should focus on encouraging depth rather thanbreadth in vocational course-taking.

Keywords: Vocational education; Career and Technical Education; Curriculum; HighSchool; Wages; NLSY97.

JEL Classification Numbers: J24, I21.

∗We owe thanks to the Institute for Research on Poverty’s Emerging Scholars Grants program and theSmith Richardson Foundation for funding and support, and to Daniela Morar and Julian Hsu for excellentresearch assistance. We also thank seminar participants at the University of Michigan and the Universityof Tennessee for helpful comments. Contact authors at: dkreisman@gsu.edu (corresponding author) andkstange@umich.edu. The usual caveat applies.

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1 Introduction

Since the publication of A Nation At Risk in 1983, policy-makers and politicians have turned

attention to a perceived educational decline of American youth. Stagnant high school grad-

uation rates, declining test scores, and signs that many college entrants are ill prepared

for college and the workforce have all contributed to this perception. Many states have re-

sponded to this alarm by increasing high school graduation requirements, typically specifying

a minimum number of years spent studying academic subjects such as English, mathematics,

science, and social studies.

These reforms have had the intended effects on curriculum: American high school grad-

uates are completing more courses in these academic subjects, and more advanced academic

coursework than they were three decades ago. However, much of these gains have come at

the expense of vocational oriented, or career and technical, education (CTE).1 In fact, be-

tween 1990 and 2009 the number of CTE credits earned by high school graduates dropped

by 14%, or roughly two-thirds of a year of vocational education, continuing a trend from the

previous decade (Hudson, 2013). This drop, shown in Figure 1, coincides with a 32% decline

in real federal funding under the Perkins Act since 1985 (the largest funding source for CTE

programs), despite large increases in federal funding for secondary school more generally

(US DOE, 2014). This trend toward academic coursework has been praised by many who

argue that vocational education in high school prepares students for “dead end” jobs and

leaves them ill-prepared for college. An opposing camp points to (perceived) shortages in

skilled professions, noting that not all students are college-bound and that for these students

vocational training may be the difference between high and low paying jobs. While evidence

exists on the relationship between high school CTE and later outcomes in the international

context (Eichhorts et al., 2015), very little evidence exists in the U.S. context. In the fol-

1The field has moved towards the use of the the term “career and technical education”, including inthe title of the 2006 Perkins Act reauthorization, to differentiate current career-focused education frompast vocational education. Throughout we use the terms “vocational education,” “career-tech,” and “CTE”interchangeably.

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lowing we evaluate these claims by assessing the relationship between vocational education

in high school, postsecondary attainment and labor market outcomes using a nationally

representative sample of U.S. high school students.

In our analysis we draw particular attention to factors that predict vocational course-

taking (choices) and the ensuing impacts (outcomes) on college-going and earnings. While

the few existing studies have found largely positive impacts of vocational course-taking on

earnings (Mane, 1999; Bishop & Mane, 2005; Meer, 2007), and mixed evidence on college

attendance (Cellini, 2006; Neumark & Rothstein, 2005, 2006), these and others frame vo-

cational course-taking as a “track” rather than as a marginal curriculum choice as is tradi-

tionally done for other subjects, particularly mathematics. Our analysis thus departs from

previous work in several regards. First, we develop a framework for curriculum choice in

which students make curriculum and college decisions dynamically in response to new infor-

mation about ability and preferences for vocational or academic (course)work. Second, we

treat vocational course-taking as marginal decision, rather than a choice of “track”, which our

empirical evidence suggests is appropriate. Third, we observe students in the labor market

to evaluate the impact of additional vocational courses on earnings, allowing for differential

returns to general vs. specialized vocational coursework. Lastly, we observe how returns vary

across vocational fields, and by local labor market characteristics.

We use detailed longitudinal transcript and labor market information for students in the

NSLY97 to examine these questions, exploiting a rich set of background and ability measures

available to us. Like prior work, we find that students with more disadvantaged backgrounds

and lower ability are more likely to take vocational courses (and fewer academic courses)

and that these courses are taken primarily in 11th and 12th grade when students have more

control over their curriculum. However, we also find modest evidence of dynamic selection:

controlling for a host of baseline student characteristics, students that perform worse in

freshman year English and math are more likely to take vocational courses later in high

school.

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We ultimately find that more vocational courses are associated with higher wages, on

the order of 1.5 to 2 log points for each vocational course-year, but that these returns are

not uniformly distributed across all vocational course-takers. Separating vocational course-

work into “higher” and “lower” levels, corresponding to introductory courses and specialized

coursework within a vocational discipline, we find that wage gains are driven entirely by

upper-level courses. We estimate no return to an additional introductory level vocational

course. Moreover, we find that these wage gains are largest for students that do not graduate

with a four-year degree, particularly males, those specializing in technical fields and those

living in (high skill density) urban areas. Interestingly, controlling for the number of cred-

its earned in other subjects and postsecondary attainment has little impact on estimates,

suggesting that displacement of academic coursework by vocational courses is minimal. The

policy implications from these findings are two-fold: first, that vocational education plays

an important role in the early careers of young, non-college graduates; and second, that

those who specialize (in technical fields) reap larger rewards. Recent trends towards more

specialized CTE concentrations (away from general, non-specialized coursework) may thus

be beneficial.

The remainder of the paper proceeds as follows. The next section sketches a Roy-type

model of curriculum choice and effects and also describes previous research in order situate

our analysis in a broader body of work. Section III introduces our data, describes the sample,

and provides descriptive evidence on vocational and non-vocational course-taking. We then

estimate the determinants of course-taking behavior in Section IV and its effects on labor

market and educational attainment in Section V. Section VI concludes with a discussion of

the policy implications of our analysis.

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2 Background and Framing

2.1 Prior work

Compared with an expansive literature on the returns to additional years of schooling, there

is relatively little evidence on the labor market consequences of high school curriculum in

the U.S., and even less work on effects of vocational course-taking in particular.2 Those

studies that evaluate the impact of vocational course-work largely treat vocational curricu-

lum as a binary choice - students are either on the “academic” or “vocational” track - as

opposed to evaluating the impact of an additional course as one might in, say, mathemat-

ics or English. Most relevant to this paper, Meer (2007) examines the relationship between

“track” (academic, general, business vocational, technical vocational) and earnings later in

life. He finds evidence of comparative advantage: students currently on a “technical” voca-

tional track would not gain from switching to an “academic” track, nor would those currently

on the academic track benefit from more vocational education.

This is not the only study to find benefits to vocational coursework. Pooling data across

several cohorts, Mane (1999) finds positive labor market payoffs to vocational coursework

and demonstrates that these were increasing through the 1980s, particularly for non-college

bound students. Analyses using older cohorts have generally supported the same conclusions

(Gustman & Steinmeier, 1982; Kang & Bishop, 1989; Bishop & Mane, 2005). Yet, selection is

an issue in evaluating vocational programs and none of these studies use exogenous variation.

Using the same data we do, Zietz & Joshi (2005) point out that participation in the vocational

track is much more common among disadvantaged students. Recent studies commissioned

by the US Department of Education as part of the national assessment of CTE required

by Perkins reauthorization used various quasi-experimental methods applied to longitudinal

2The value of vocational education has also been subject to debate in both developing and transitionaleconomies, with prior research finding minimal to positive effects on education and labor market outcomes(Malamud & Pop-Eleches, 2010; Attanasio, et al., 2011; Eichhorst, et al. 2015).

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student-level data from two locales (Philadelphia and San Diego), one state (Florida), and

one nationally-representative high school cohort (ELS 2002). These studies found mixed

evidence of the effects of CTE on secondary, postsecondary, and labor market outcomes (US

DOE, 2014). Taken as a whole, evidence on the effects of vocational coursework is mixed.

A related body of work explores the effect of participation in “School-to-Work” (STW)

programs on education and labor market outcomes using early waves of the NLSY97. This

literature has evolved mostly in parallel to the above mentioned literature on high school

curriculum and vocational education, which is surprising since School-to-Work programs can

be seen as a close substitute, in purpose if not structure, to traditional vocational coursework.

The results, again, are mixed. Neumark & Joyce (2001) find little effect of STW participation

on “behavior associated with future college attendance”, but find an effect on students’ beliefs

that they will complete high school and in beliefs about future labor market participation.

Neumark & Rothstein (2005, 2006) find some positive benefits on college attendance and

the likelihood of later employment, but that these accrue largely for males and for programs

with direct links to the labor market (internships and cooperative education); they in fact

find some negative effects from tech-prep. Cellini (2006) uses a sibling fixed effects model

and focuses only on tech-prep, finding that participants are more likely to complete high

school and attend a two-year college, resulting in a higher overall attainment, but that this

comes at a cost of a decline in 4-year college attendance.

A separate literature has evolved which estimates the consequences of high school cur-

riculum choice, much of which is summarized in Altonji, Blom, & Meghir (2012). In the U.S.

much of this work focuses specifically on mathematics course-taking (Altonji 1995; Levine &

Zimmerman, 1995; Rose & Betts, 2004; Goodman, 2012), generally finding that more math

has a positive association with earnings. Importantly, unlike the literature on returns to

taking a particular “track”, this literature treats each course as a decision on a continuum,

allowing for marginal as opposed to discrete changes in curriculum choice. We build on this

work by explicitly examining both high school curriculum choice and outcomes, with a focus

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on the number of vocational courses students choose to take.

Blending these two literatures, we treat curriculum as a continuum, a decision informed

by Figure 2, which depicts cumulative distribution functions for the total number of voca-

tional credits and the share of credits that are vocational for respondents in the National

Longitudinal Survey of Youth (1997). Figure 2 suggests (i) that vocational courses account

for a non-trivial share of high school coursework, (ii) that an overwhelming majority of stu-

dents take at least one vocational course in high school, and (iii) that the distribution is

smooth, rather than bimodal, as strong “tracking” would imply. We frame the decision to

take vocational course as the result of students learning about their comparative advantage

and preferences for vocational or academic (course-)work. In response to baseline expecta-

tions and received signals about “fit”, students rationally decide whether to pursue a more

vocational or academic focused curriculum as high school progresses. Students consider both

human capital and informational consequences of their choices, as curriculum also provides

information about labor market prospects. While several studies of college persistence and

major choice have this dynamic flavor (e.g., Stinebrickner & Stinebrickner, 2011; Stange,

2012), this has been mostly absent from studies of high school curricula. Related, Malamud

(2011) finds that exposure to a broader curriculum reduces job switches later in life (inter-

preted as “mistakes”) by improving match quality at the expense of greater specialization.

2.2 Theoretical Framework

To frame our empirical analysis, we consider a stylized dynamic model of high school curricu-

lum choice, college enrollment, college completion and labor market entry in which students

are forward-looking but face uncertainty about their ability.

We assume students are endowed with ability along two dimensions: academic ability αa

and vocational ability αv. These endowed abilities are augmented by investments in human

capital through curriculum choices manifested in the number of academic and vocational

courses students take, A and V respectively. We then define human capital stock K at time

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t as a function of ability and curriculum choice, including college:

Kt = ka,t(αa, A), kv,t(αv, V ) (1)

We assume academic ability (αa) influences performance in academic subjects in high school

and in college, and the likelihood of college graduation, while vocational ability (αv) in-

fluences performance in vocational subjects in high school. Neither component of ability is

known at the start of high school, though students have prior beliefs, which depend on fixed

characteristics (X) we assume are observed by the econometrician. Students learn about

αa and αv via performance in academic and vocational subjects respectively manifested in

grades conditional on covariates - i.e. grades relative to what would be expected. For instance,

students that perform better in academic courses than their baseline characteristics would

predict will revise their belief about αa upward. These posterior beliefs then influence stu-

dents’ subsequent decisions about high school curriculum, graduation and college enrollment.

Four-year college graduation then depends on academic capital, pr(college) = g(ka, X), in-

cluding high school curriculum and ability, and covariates observable to the econometrician.

We assume graduation from a two-year institution can depend on kv as well.

We define the labor market as having multiple sectors, delineated by highest degree

completed, which define the distribution from which workers draw wages. Both ability com-

ponents impact productivity in the labor market, though the importance of each may vary

by sector; that is, ka may be more valuable in the college-educated labor market while kv

may be more valuable in the non-four-year college educated sector. Thus, (log) wage at time

t in sector j depends on accumulated skills that are a function of ability and education –

ka,t(αa, A) and kv,t(αv, V ) – plus covariates such as work experience, year and local labor

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market conditions.

wjt = f(ka,t, kv,t, X) (2)

for j = {drop out, H.S., college}.

During high school students receive utility which depends on curriculum (i.e. mix of aca-

demic and vocational coursework) and expected performance in the chosen curriculum. Util-

ity during college depends on expected performance in college, covariates, and high school

curriculum. Utility in the labor market depends only on log wages here, but might also in-

clude a hedonic preference for job type. Students sequentially choose high school curriculum

(or dropout) and then whether or not to enroll in two- or four-year college to maximize the

present discounted value of lifetime utility. Before each of these decisions, students update

their beliefs about own academic and vocational ability.

The model delineates several tradeoffs in the choice of curriculum. First, high school

vocational courses could have lower psychic costs than academic ones for students with

high αv (low αa). Second, they could be productivity-enhancing for students that enter the

non-(four-year) college job sector. Third, they could provide students with the opportunity

to acquire more information about αv, which should enable better decisions about college

enrollment. Productivity in the non-college sector is an important factor in college enrollment

decisions, yet many students may lack individual-specific information about this parameter.

Exposure to vocational curriculum may be one way to acquire it.

However, vocational coursework could have drawbacks. If the vocational track is less rigor-

ous, it may leave students ill-prepared for college and reduce the likelihood of graduating col-

lege among those that enroll. Additionally, vocational concentrators may experience reduced

productivity in the college job sector if academic coursework is more productivity-enhancing

than vocational among college graduates. Similarly, reduced collegiate performance may also

mean that vocational students have a greater psychic cost of going to college than those

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on the academic track. Lastly, students on the vocational track will necessarily have less

information about αa than those that pursue the academic track. This may cause some to

forego college (and the subsequent returns) who would benefit from enrolling.

One implication of the model is that providing the option of studying a vocational cur-

riculum has ambiguous effects on many outcomes overall since the effects are likely to be

quite heterogeneous. For students with diffuse priors about their abilities, exposure to voca-

tional coursework could either increase or decrease college enrollment depending on whether

estimates of αa and αv are revised upwards or downward. Regardless, the additional option

should improve welfare even among those who reduce college enrollment rates because it

reduces dropout (i.e. bad matches between students and college).

3 Data Sources and Sample

3.1 The NLSY97 and Transcripts

We examine schooling and labor market activity for respondents in the National Longitudi-

nal Study of Youth 1997 cohort (NLSY97). This data includes 8,984 individuals who were

ages 12 to 18 when they were first interviewed in 1997. The survey is representative of all

American youth at that time period and respondents have been followed annually with in-

formation on educational attainment, labor market experience and family formation. High

school transcripts were collected from respondents’ high schools in two waves by the National

Opinion Research Center (NORC), who administers the NLSY. The first was conducted in

1999-2000 for 1,391 respondents who were no longer enrolled in high school at that time.

Transcripts for 4,618 additional respondents were collected in 2004. In total, transcript data

were collected for 6,009 respondents.

NORC used course catalogs to categorize all transcript course titles to a common scheme

according to the Secondary School Taxonomy (SST-R). To create uniformity in credit taking,

courses are converted into “Carnegie credits” where one Carnegie credit is defined as a course

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that meets every day for one period for an entire school year. Similarly, grades for each course

were converted by NORC into a common (0-4) metric.3

Importantly, this careful course coding allows us to disaggregate courses into “low” and

“high” level courses. For vocational courses, lower level courses are those classified as “1st

course” on transcripts. Upper level courses include courses beyond the introductory level,

including: “2nd or later courses”, “Specialty course”, or “Co-op/Work Experience” in the

transcripts. This disaggregation allows us to analyze the benefits of breadth – taking many

courses in different fields – and depth – specializing in a particular vocational field. We con-

duct an analogous disaggregation for core courses (English, math, science and social studies)

with the purpose of separating more and less difficult versions of a particular course within a

subject. In this case, transcripts identify courses as either “basic”, “regular”, “Advanced and

Honors”, “Specialized” and “AP/IB”. In this case, lower courses are “basic” or “regular” and

higher courses are the remainder.

3.2 Sample definition

We restrict our sample to exclude those lacking transcripts in all years of high school, those

who never completed 9th grade (for whom high school curriculum is not relevant), and

respondents with extreme or illogical totals in their transcripts. Specifically, we exclude high

school graduates who either (i) had fewer than four years of high school transcripts; (ii) had

taken fewer than two courses in each of math, English, science and social studies; or (iii) had

more than 36 or fewer than 20 total credits. For high school dropouts, the limiting conditions

are necessarily more relaxed as any small number of credits can be consistent with dropout.

Thus, for dropouts we restrict to those who (i) completed at least 9th grade; (ii) had taken

at least one-half course in math, English, science and social studies; and (iii) had taken more3For the wage analysis we construct earned credits as those for which students either received a passing

(non-F) or satisfactory mark in the course. For the course-taking analysis credits attempted are the creditsa student would have earned had she passed the course. In cases where the number of Carnegie credits isnot reported, we impute Carnegie credits as the modal number of credits that a particular student earnedin courses in the same field. When a student lacks a comparable course, we then impute to the modal valueof all students taking the same exact course title in the same grade in the full sample.

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than four total credits ever. The resulting “Analysis sample” used to study curriculum choice

and postsecondary outcomes includes 4,414 respondents. Lastly, we restrict wage analysis to

a “Wage sample” of 3,796 students whom we determined entered the labor market with a

valid wage record (non-self employed) after and including age 23. To determine labor market

entry we use monthly enrollment arrays from the NLSY97 and determine enrollment in each

semester (fall, spring). We then define labor market entry as the first of four consecutive

non-enrolled semesters that are followed by no more than one enrolled semester thereafter.4

Table 1 compares the Analysis and Wage samples to the full NLSY among a broad set of

demographic characteristics. Comparing columns 1 and 2 shows that the Analysis sample is

slightly more advantaged than the full NLSY, but that on average is reasonably representative

of the NLSY in whole. Importantly, differences between the two are largely determined by

whether NORC could retrieve transcripts, rather than any behavior of the respondent herself.

A comparison of columns 2 and 3 shows that the Wage and Analysis samples are nearly

identical with few respondents lost between the two. Our empirical exercises focusing on

wages pool multiple observations for each individual over time, resulting in 20,572 person-

year observations.

Approximately one-third of our Analysis sample obtain a four-year college degree, one-

third attend college without earning a degree, 8 percent earn a two-year degree, 17 percent

obtain a high school diploma but no post-secondary education, and 6 percent do not graduate

from high school.

3.3 The distribution of course-taking

Table 2 shows student characteristics, credits taken, and outcomes separately by tercile of

vocational course-taking. We find that average course-taking patterns mask considerable

variation; some students pursue a very academic curriculum while others take many voca-

tional courses, even among high school graduates. Students in the highest tercile take more4We do not restrict wage observations by the number of hours worked, but in a robustness check we

restrict to 30+ hour per week jobs and find nearly identical results.

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vocational courses than either high-level core courses or electives. Furthermore, students

with a more vocational curriculum have worse postsecondary outcomes; 43 percent of stu-

dents in the lowest CTE tercile complete a four-year degree compared with only 23 percent

of those in the highest tercile. However, curriculum is clearly related to other factors likely

to influence these outcomes; specifically, vocational concentrators are more likely to come

from disadvantaged backgrounds and have lower AFQT scores (an aggregation of math and

English components of Armed Services Vocational Aptitude Battery, or ASVAB, exam), a

proxy for ability.

Figure 3 shows the distribution of credits taken by field. It is clear from this figure

that not only is there a considerable amount of variation in vocational course taking com-

pared with other core and non-core coursework, but also that the modal value is greater

than zero. The extent of vocational coursework taken by U.S. high school students rela-

tive to traditional academic subjects, even among the most academically-oriented, has not

been previously appreciated. Importantly, we interpret the smoothness of the distribution

of vocational course-taking in Figures 2 and 3 as confirmatory evidence of our treatment of

vocational education as a marginal decision as opposed to a discrete choice of “track”.

Figure 4 depicts the average number of courses taken by our sample in each vocational cat-

egory. The most common types of vocational courses taken are technology/industry, business

and management, computer technology and keyboarding, and general (general labor market

preparation). (Table A1 in the Appendix describes the full list of vocational courses in the

NLSY97.) Advanced vocational courses are offered and taken in each of these categories

except for general labor market preparation and keyboarding. Given this substantial hetero-

geneity in high school curriculum, it is natural to investigate the sources and consequences

of this variation.

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4 Choices: Explaining Course-taking Behavior

4.1 Descriptive evidence: Course-taking

We now turn our attention to the predictors and temporal pattern of vocational course

taking in high school. Figure 5 presents the average number of courses taken by subject, sep-

arately by grade. Though English and foreign language are taken with the same frequency

at all grades – students take about one English credit and 0.5 language credits per year,

on average – others exhibit strong time trends. For instance, vocational courses are taken

with much greater frequency in 11th and 12th grades, as students approach graduation and

ostensibly have more control over their schedules. Social studies courses exhibit a similar,

though weaker, trend. On the other hand, math and science course-taking drops consider-

ably in 12th grade. This suggests that there exist opportunities for students’ experiences

early in high school to inform later curriculum choices, and that students specialization in

vocational coursework has a distinct temporal aspect. This temporal pattern is an important

feature of our conceptual model, where early course outcomes inform beliefs about ability

and subsequent decisions.

Figure 6 plots the number of credits taken by subject area separately by AFQT quartile.

Though total credits are comparable across AFQT scores, there exists substantial variation

in subject mix. Vocational courses and foreign language courses exhibit the most noticeable

relationship with AFQT. High AFQT students take about two-thirds as many vocational

courses as low AFQT students and about three times as many foreign language courses.

Higher AFQT students also tend to take more science courses. It is important to note that

for most students the AFQT is administered during their high school careers – the mean and

median respondent took the AFQT approximately at age 14.5 and nearly all had take the

test by age 16 – thus we cannot rule out the impact of course-taking on the AFQT itself.

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4.2 Empirical specification: Course-taking

To examine the correlates of vocational course taking more rigorously, we estimate reduced-

form linear regression models using the number of credits taken in each subject, j, as the

dependent variable. In each specification we focus on vocational courses taken in 10-12th

grade, as there are greater opportunities for students to choose vocational courses after

freshman year and this allows us to model choice in grades 10-12 as a function of revealed

ability (grades) in 9th grade.5 Guided by the theoretical framework outlined earlier, we are

interested in three classes of explanatory variables, which we enter into the model sequen-

tially. First, the vector Xi includes observed characteristics of the student, such as gender,

race, family background and 9th grade cohort as described in the summary statistics tables.

Second, we include the AFQT score. Since we expect curriculum decisions to be influenced by

new information about students’ “fit” with academic or vocational heavy curricula, we also

include measures of academic performance in math and English in 9th grade, GPAcore9th

i ,

and a control for whether the math or English courses were high (difficult) or low level,

High/Lowcore9th

i .6 Though students have prior beliefs about their fit with different curricu-

lums, performance early in high school provides additional information with which expecta-

tions can be revised. Students that perform well in early core academic subjects might then

pursue a more academic (or less vocational) curriculum. Our baseline specification is:

V ocCreditsi = γ0 + γ1Xi + γ2AFQTi + γ3GPAcore9th

i + γ4High/Lowcore9th

i + εi (3)

The estimated parameters combine several of the structural parameters described in the

dynamic model. In particular, the relationship between AFQT and number of vocational

credits taken (γ2) combines the effect of ability on expected performance in high school

and college (which influences the flow utility from curriculum), on the likelihood of college

5Results are similar if we include 9th grade course-taking in columns 1-2.6Including math and English GPA’s separately yielded empirically equivalent results.

15

graduation, and on labor market outcomes. Thus, the fact that high ability students are less

likely to take vocational courses may be explained by any of these mechanisms.

We interpret γ3 as the importance of new information revealed about “fit” with an aca-

demic curriculum in the form of 9th grade GPA. However, if GPAi, conditional on other

covariates, is known to individuals but not the econometrician, then this parameter may in-

stead capture unobserved heterogeneity rather than learning about uncertain ability. Though

our covariates are rich, we cannot entirely rule this explanation out with a reduced-form ap-

proach.

4.3 Results: Course-taking

Table 3 presents results for vocational course taking. As prior work has documented, we

find that male students and those from disadvantaged backgrounds (mother has high school

degree or less, low income, received public assistance) are more likely to take vocational

courses. Vocational course-taking also decreases sharply with measured ability (AFQT).

This is true even when high school dropouts are excluded in specification 4. As noted above,

vocational course-taking has a strong temporal dimension: it is taken largely in 10th and

12th grade. Thus, information revealed during high school could potentially influence course-

taking decisions over time. We test this in specification (3), which includes 9th-grade GPA

in math and English as a covariate plus controls for whether these courses were upper or

lower level. We find that students performing poorly in these academic subjects, conditional

on our rich set of covariates, are more likely to take vocational courses later in high school,

even controlling for AFQT. That is, a one-point increase in 9th-grade math and English

GPA decreases total vocational course taking by 0.14 of a one-year course. This represents a

roughly 5 percent decrease in vocational course-taking off of a base of approximately 3 total

vocational courses on average. This result is consistent with a key aspect of our theoretical

framework, where students use early course performance to update their beliefs about their

fit with the academic/college sector or vocational/non-college tracks, and these beliefs in

16

turn influence course-taking.

The final two columns present estimates separately for low and high vocational course-

work respectively. Results in these final two specifications reveal a subtle yet important dis-

tinction – that ability and freshman year GPA, in addition to many of the fixed demographic

characteristics we observe concerning childhood circumstances, are far more predictive of tak-

ing low-level vocational courses than they are of upper-level or specialized vocational courses.

In particular, we find that a negative signal of ability manifested in low grades in core courses

in 9th grade, conditional on course level and AFQT, increases the number of low-level vo-

cational courses in grades 10-12, but has no impact on taking upper level, or specialized,

vocational courses. Put differently, low ability, or learning about low ability, leads students

to take a broader array of vocational courses rather than to specialize in a particular type

of vocational training. The importance of this result is underscored in subsequent analyses

below demonstrating that (i) while lower level vocational courses deter students from col-

lege, upper level courses at worst do no harm, and (ii) that while upper level vocational

courses benefit students in the labor market, low-level vocational courses have no impact.

We interpret this in terms of gains to specialization.

The implications of this analysis for analyzing the impacts of vocational curriculum on

outcomes are thus three-fold. First, high-school curriculum is clearly not randomly assigned.

Students that take more vocational courses are very different in observed characteristics

than those that take fewer. Second, course heterogeneity is extremely important. Vocational

courses are quite different, ranging from keyboarding courses to industrial technology courses

(e.g. drafting, metal working, machine repair) to general labor market preparation courses;

these different types of vocational courses have different opportunities for specialization.

Thus, accounting for this heterogeneity within the broad category of vocational coursework

and across students is thus likely to be important. Third, high school curriculum may not be

pre-determined when students enter, as students’ choice of curriculum appears to respond to

new information obtained during 9th grade, and likely at other times. Thus, controlling for

17

factors that influence curriculum choice over time, rather than fixed student characteristics,

may be important.

5 Outcomes: College and the Labor Market

5.1 Empirical specification: College attendance and completion

We begin our analysis of the consequences of high school vocational coursework by observing

the relationship between high school curriculum choice and both college attendance and com-

pletion. To do so, we first estimate three linear reduced form equations where the dependent

variable, College, is a binary indicator of college attendance in a two-year college, a four-year

college and then any college. We then repeat this exercise for college completion conditional

on college enrollment. In both cases we condition on having completed high school.

Collegei = β0 + β1V ocLowi + β2V ocHighi + β4CoreLowi + β5CoreHighi + β6Electi (4)

+ β7Xi + β8AFQTi + β9GPA9 + εi

As in Equation 3 above, AFQT is a proxy for skill and GPA9 measures GPA in math and

English in 9th grade, including controls for whether math and English courses were upper

or lower level. Each of the course indicators counts the total number of courses respondent

i took of each type. The vector Xi contains our full set of covariates.

5.2 Empirical specification: Wages

To examine labor market consequences of vocational course taking we stack wage observa-

tions and estimate reduced-form linear regression models that exploit cross-sectional varia-

tion in high school curricula. Our primary labor market outcome is the log of (real) hourly

wage, which is observed at multiple ages and thus pooled across all observations for individ-

uals in our wage sample. Our main explanatory variables, V ocHighi and V ocLowi, measure

18

the number of high and low vocational credits earned during high school. To address the

selection problems highlighted in the previous section, we control for an increasingly rich set

of student background and ability characteristics that may influence high school curriculum

and also be correlated with outcomes.

Since number of vocational credits is likely correlated with credits earned in other sub-

jects, in final specifications we control for a vector of credits earned in core (high and low)

and elective subjects (language, art/music, PE, and “other” courses). Similarly, some spec-

ifications control for years of postsecondary schooling and degrees earned. The inclusion of

either of these types of controls (non-vocational courses and postsecondary outcomes) alters

the interpretation of the estimated parameters and thus is an important modeling choice, a

subject we discuss more thoroughly in the next subsection. Our preferred specification is:

ln(wage)it = β0 + β1V ocLowi + β2V ocHighi + β4CoreLowi + β5CoreHighi + β6Electi (5)

+ β7Xi + β8AFQTi + β9GPA9 + β10Ageit + ψPostseci + τt + ωm + εit

Our controls include all the demographic and high school characteristics listed in Table 2,

as well as age when the wage is reported and secular year effects (ηt). Some specifications

include characteristics of the geographic region (ωm), including state or MSA fixed effects. All

specifications account for the within-individual correlation across observations by clustering

standard errors by individual.

5.3 An aside: Course substitution, postsecondary outcomes and the

interpretation of parameter estimates

Since students’ time is fixed, one substantive modeling choice is whether and how to control

for the number of credits earned in other subjects. Without controls for other courses – i.e.

core courses and electives – the parameters β1 and β2 in Equation 5 should be interpreted

as the change in log wage associated with an additional (high or low) vocational credit, con-

trolling for selection on observables. Since each additional vocational course could be taken

19

at the expense of another course (math, language, science, etc.), or what would otherwise be

non-course time, the nature of the treatment likely varies across the population. The three

panels in Figure 7 depict the implicit nature of the tradeoff between vocational and other

types of courses. Vocational courses are most clearly traded off with electives (art/music, for-

eign language, physical education, and “other” courses). The correlation between vocational

and core academic courses (English, math, science, and social studies) is also negative, but

much weaker. Thus, the “average treatment” in the model without controls for other courses

can be thought of as taking one vocational course but fewer electives.

Controlling for number of credits taken in other subjects alters this interpretation. As

a thought experiment, imagine that we divided courses into three types: core, vocational

and electives, and then for each respondent determined what share of total coursework each

constituted. Including all three measures in our empirical specification would violate the full

rank assumption (perfect collinearity) and one term would have to be omitted in the regres-

sion equation. Thus, holding course-taking in all other subjects constant, the parameters β1

and β2 should be interpreted as the outcome change associated with taking one more high or

low vocational credit at the expense of what would otherwise be non-course time, controlling

for selection on observables. That is, the full rank assumption relies empirically on variation

in the total number of courses taken across students. In this case, the opportunity cost of

an additional course depends on how students potentially use this marginal hour among

available activities, such as study hall, paid or volunteer work, homework, or leisure. Fur-

thermore, controls for course taking in other subjects may also account for additional forms

of selection if course-taking in other subjects is a marker for traits influencing outcomes that

also correlate with vocational course-taking.

Surprisingly, prior literature on the effects of course taking is mostly silent on this im-

portant conceptual issue, with the exception of Altonji (1995). His models include number

of credits taken in eight subjects (English, social studies, math, science, foreign language,

fine arts, industrial arts, and commercial), omitting time devoted to study halls, certain

20

vocational courses, PE, and home economics. He concludes that “one would expect all the

elements of [the coefficient vector] to be greater than or equal to 0,” since the specification

controls for any displacement effects on other course-taking. In examining the labor market

effects of math courses, Rose and Betts (2004) do not mention non-math course taking (in

vocational, other academic, or elective courses) as the primary opportunity cost of taking an

additional math course. Their main empirical model includes the number of math courses in

six different levels and thus each coefficient should be interpreted as the effect of taking more

courses in one level of rigor holding constant the number of math courses in the other levels.

In these models students are explicitly trading off math course-taking with time allocated

to other subjects or non-school time – which of these, though, remains unclear. Subsequent

specifications include number of courses in English, science, and foreign language, also us-

ing detailed curriculum categories. Interpreting the coefficients in these models is difficult

because an additional year spent in upper-level English, for instance, may have a different

opportunity cost (against which the effect is measured) than one spent in basic Biology.

Goodman (2012) shows that curricular reforms have a moderate and significant impact on

math course-taking, but also a modest (insignificant) impact on other course-taking. His

two-sample instrumental variables estimates of the wage effects of math courses implicitly

assumes that reforms cannot impact wages through these non-math courses either because

reforms do not affect non-math course-taking or because such courses have no labor market

impact. In our preferred model we follow in the spirit of Altonji (1995) and include a vector

containing number of credits earned in all other subjects, but show similar specifications

with only vocational credits included. In an ancillary set of regressions we model the share of

courses taken in each subject, leaving electives as the omitted category. While directionality

in each of these specifications is similar, leading to similar policy conclusions, for reasons

mentioned above interpretation differs.

A separate concern is the treatment of college attendance and completion in wage speci-

fications. The prior literature has paid closer attention to the question of whether to control

21

for postsecondary outcomes, such as college enrollment and degree completion. One channel

that high school curriculum could impact labor market outcomes is by altering postsecondary

attainment by, for instance, increasing collegiate aspirations or readiness. Most prior studies

control for postsecondary attainment, interpreting any change in coefficient as evidence of

whether curriculum’s effect is direct (independent of attainment) or whether it works primar-

ily via altering educational attainment. Rose & Betts (2004) estimate that almost two-thirds

of the impact of math courses appears to work through educational attainment. We follow

in this tradition but show outcomes both without accounting for ultimate degree attainment

and then by highest degree attained for completeness.

5.4 Results: College attendance and completion

Before examining labor market outcomes, Panel A of Table 4 presents estimates of effects

on postsecondary attendance and completion among high school graduates as described in

Equation 4. We find that each additional year of introductory (low) vocational coursework

decreases the likelihood of attending any postsecondary institution by approximately 1 per-

centage point conditional on attending high school. While none of the other coefficients

concerning vocational course-taking in columns 1-3 of Panel A are statistically significant

at conventional levels, their direction and magnitude suggest that more introductory level

vocational coursework decreases college attendance across the board. Yet, the sign of upper

level vocational coursework on the likelihood of attending a two-year college is positive, but

indistinguishable from zero. Repeating the same exercise in columns 4-6 for respondents who

attended a two-year, four-year or any college as their highest grade attended, suggests that

there is no evidence that vocational courses decrease graduation rates for those enrolled.

Taken together, these results suggest that vocational courses may deter the marginal stu-

dent from college, but that there is no aggregate impact on graduation rates. Put differently,

vocational courses appear to pull those students out of college who are less likely to graduate.

Though noisy, this is suggestive of a learning process where vocational coursework provides

22

students with possibly valuable labor market skills and additional information about their

comparative advantage in the vocational or college labor market.

In Panel B of Table 4 we replicate our model using the share of credits accumulated

in each field and include an additional control for the total number of credits accumulated

(estimating the same model without TotalCredits yields empirically similar results). In this

case, as an actualization of the thought experiment described above, we omit the share of

courses that were electives to satisfy the full rank requirement. Results are similar to Panel

A, at least in direction. We find that while increasing the share of low vocational courses

decreases college attendance for four-year schools, increasing the share of courses dedicated to

specialized, or upper level, vocational course-work increases enrollment to two-year schools,

but decreases four-year enrollment. Concerning college completion conditional on enrollment,

we again find that upper level vocational courses improve two-year graduation, but not four-

year. It is important to note here that attendance and completion are defined as by highest

degree, therefore any students who attended a two year school and subsequently attended a

four institution would be counted as four-year attenders and/or graduates, though the final

columns (3) and (6) account for this in aggregate.

5.5 Results: Wage regressions

We know that that the intensity of vocational courses in high school is negatively correlated

with wages later in life; Panel C of Table 2 demonstrates this. This is not surprising as

high school graduates that take more vocational courses tend to be lower-achieving and

more disadvantaged, attributes that have independent effects on labor market outcomes.

Thus, most factors influencing selection tend to create a negative bias. Table 5 presents our

main findings on labor market outcomes, which control for many of these factors. While

unconditional means show a negative association between low vocational credits and wages,

controlling for background characteristics entirely accounts for this as we move from columns

(1) to (2). Yet, the positive gains associated with an additional upper level vocational course

23

remain positive, suggesting a nearly 2% increase with each additional course. Specification

(3) includes AFQT and GPA in 9th grade core courses with no impact on results. Column

(4) then controls for the number of credits earned in other subjects, separating elective

and core academic subjects (math, English, science and social studies). This specification

also distinguishes high- and low-level academic subjects (e.g. advanced algebra vs. basic

math). Consistent with results in Rose and Betts (2004), advanced coursework is much more

advantageous than basic coursework.

Comparing columns (2) and (4), the coefficient on advanced vocational work does not

change, suggesting that vocational course-taking does not crowd out other courses, which

also have impacts on wages. Alternatively, credits earned in other subjects may be a marker

for academic ability which further controls for the negative selection bias. Since these factors

may offset, we are not able to distinguish these two distinct channels. Controlling for all

factors in column (5) has little impact of results other than reducing the gain to upper-level

core courses by one-third.

High school dropouts have fewer vocational courses and lower wages, causing us to over-

state the direct effect of vocational courses on wages if high school graduation is not con-

trolled for. However, Table 3 suggested that the “indirect” effect of vocational course-taking

via postsecondary attainment is likely to be negative since vocational coursework is associ-

ated with lower postsecondary participation. Specification (6) addresses both of these issues

by controlling for secondary and postsecondary attainment. Consequently, the estimated im-

pact of advanced vocational coursework decreases slightly to 1.6%. Interestingly, it appears

that the benefits of advanced academic coursework operate primarily through postsecondary

outcomes since the positive wage effect is eliminated once attainment is accounted for. In

column (7) we restrict the sample to jobs for which respondents reported working at least

30 hours per week to restrict observations to “full time” employment. We find that this

restriction has virtually no impact on results.

In Table 6 we repeat our preferred specification from Table 5 using an alternative spec-

24

ification modeling the share of courses taken rather than the count as we did in Panel B

of Table 4. Again results largely confirm our preferred specification, but with a different

interpretation. In Table 6 we estimate that a one percentage-point increase in the share of

upper level vocational courses increases wages by one-half percent, even after controlling for

a full set of controls. To put these results in comparable terms, we estimate the change in

share associated with a one course increase by regressing the share of upper level vocational

courses on the number of vocational courses taken, finding that one extra course is equiv-

alent to a nearly four percent increase in the share of courses taken. Adding a full set of

covariates to the regression has no impact on this estimate, suggesting that variation in total

credits is not strongly correlated with the distribution of where credits are allocated. Since

students in the wage sample take roughly 25 courses on average, where a one course increase

is equivalent to a four percentage-point change, this confirms our intuition of a tradeoff with

elective courses. Multiplying this four percentage-point increase by the 0.005 coefficient esti-

mate suggests a two percent increase in wages - only marginally larger than the 1.6 percent

increase estimated in the credit count model in Table 5, suggesting that these results do not

rely entirely on functional form assumptions. For the remainder of the paper we rely on our

preferred specification using counts of all courses taken by type, rather than shares.

5.6 Heterogeneity of Wage Effects

In Table 7 we address one potential source of omitted variable bias: characteristics of the

local labor market students would experience after high school. To do so, we re-estimate

Equation 5 above including either State or MSA fixed effects, or a measure of the fraction

of occupational employment requiring different levels of education. This measure of occupa-

tional employment is determined by the fraction of MSA employment that is in occupations

requiring a BA, AA, high school diploma, or no high school diploma, obtained from the 2000

Occupational Employment Statistics program from the Bureau of Labor Statistics. For each

NLSY respondent, this measure is taken from the MSA that respondent lived in during 12th

25

grade (to avoid endogenously chosen post-schooling labor markets), provided in confidential

NLSY geocode data obtained by special license. We find that in each case our estimates

presented in Tables 5 and 6 are a lower bounds and that curriculum effects are quite robust

to controls for the characteristics of the local labor market.

We extend this analysis in Table 8 by examining wage premiums by the type and char-

acteristics of the metro area in which students attended high school. In columns (2)-(4) we

repeat our main specification by MSA type, finding that returns are higher in urban areas.

The final three columns (6)-(8) interact vocational course taking with the share of MSA em-

ployment that is in occupations requiring a BA degree.7 We interpret this as a general index

of the skill content of occupational employment. Though estimates are very imprecise, the

point estimates are directionally consistent with the notion that advanced vocational course-

work is more useful in areas that have more skilled employment, even when attributes of

MSA’s are accounted for via fixed effects. That is, the within-MSA wage difference between

students with one vs. no advanced vocational courses is larger in MSA’s that have more

skilled occupational employment than those with less-skilled employment. Interestingly, the

opposite is true for introductory (low) vocational courses: these courses are less useful in

MSA’s with more skilled occupational employment.

We may also expect that vocational education is particularly useful for students that

do not obtain a four-year college degree. Table 9 re-estimates our main outcome model

separately by post-secondary attendance and completion, and for high school dropouts, sep-

arately by gender. We in fact find that the positive return to advanced vocational education

is primarily driven by students that do not ultimately earn a four-year degree, particularly

males. Estimated wage benefits of advanced vocational course work are greatest for students

with some college or a two-year degree (though estimates for high school dropouts are also

large, but imprecise).

Lastly, we decompose these observed wage gains by vocational field of study, though sam-

7The share of employment in occupations requiring a BA degree comes from the 2000 OES.

26

ple size constraints prohibit us from a full set of interactions between occupational field and

high/low denomination. These results are shown in Table 10 where coefficients are ordered

from the most to least frequented vocational courses. We find in the full specification(column

2) that gains are largest for credits earned in Health Care and Technology & Industry, and

we find earnings are negatively associated with credits accumulated in the most general

labor market prep courses (GLMP Industrial Arts and GLMP Other).8 It is important to

note that these two courses are the least frequented by students, averaging less than 0.02

credits in total. Alternately, Tech & Industry, which is associated with the largest gain along

with Health Care, is also the most commonly frequented course, followed by Keyboarding,

which has no labor market return - a non-trivial finding considering that computer access

was far from universal in the late 1990’s and early 2000’s when NLSY respondents were in

high school.

6 Conclusion and Policy Implications

Using detailed information on high school curriculum, postsecondary choices and attainment,

and labor market outcomes from the NLSY97, we find that one additional year of advanced

vocational coursework during high school is associated with a 1.6 percent increase in wages.

This result is robust to an extensive set of individual controls for student background and

ability, for any displacement effects on the completion of other courses, for effects on postsec-

ondary attainment, and for controls for the occupational demands of the local labor market.

The benefit of vocational education is particularly large for high school graduates with some

college but no bachelor’s degree, men, and for students residing in metro areas and areas with

a greater skill content of the local workforce. Thus, the criticism that vocational education

is “preparing students for jobs that don’t exist” is not supported by our analysis. Rather, we

find that advanced vocational work is equipping students who likely will not earn a BA with

valuable labor market skills.8GLMP is General Labor Market Preparation.

27

How secondary schools should prepare students for post-graduation – college, the work-

force, citizenry – remains a timely policy question. Many states are still altering their curricu-

lum requirements, with most implementing higher academic standards often at the expense of

vocational offerings. The promotion of national Common Core standards by many states con-

tinues this trend. Given the labor market benefits of vocational coursework for students that

do not obtain a four-year college degree, a shift towards more rigorous academic standards

may adversely affect some students that would be better served by vocational coursework.

Beyond direct labor market effects, exposure to a vocational curriculum may also have

informational value, enabling students to make more informed choices about their likely fit

with college. Since taking more advanced vocational coursework is associated with lower

four-year college enrollment rates but no reduction in college completion, this implies that

students induced out of four-year college by a vocational secondary curriculum would likely

not have completed a degree. Early exposure to vocational curriculum may thus facilitate

better college enrollment decisions and fewer ex-post mistakes. Our analysis also reveals

that students dynamically select into high school curriculum: early performance in academic

subjects are predictive of more intensive vocational education later in high school. This

implies that important information about students’ best fit with either the academic or

vocational track is only revealed as high school unfolds. High schools that expose students

to diverse offerings early may thus facilitate better matches between students and sectors.

The primary limitation of the current study is that our labor market analysis relies on a

selection-on-observables assumption in order have a causal interpretation. While the NLSY

is uniquely rich in student-level covariates, remaining unobserved factors that influence both

curriculum choice and labor market success may bias our estimates. Future work should

better address this selection problem by either explicitly modeling the selection process dy-

namically or by exploiting quasi-experimental variation in curriculum. Recent studies that

exploit oversubscribed vocational programs (Dougherty, 2014) are a fruitful direction. Fu-

ture work should also examine a number of different outcomes that our model suggests may

28

be important, such as employment and employment volatility, and the likelihood of ex-post

“mistakes”, such as enrolling in college and dropping out. Finally, we necessarily study the la-

bor market consequences of CTE coursework as it existed more than a decade ago. While this

is still more current than much prior research, the nature of CTE education is continuously

evolving. CTE is shifting towards fields such as health sciences (away from manufacturing)

and into structured “Programs of Study” that better connect secondary and postsecondary

curriculum and with industry-recognized credentials (US Department of Education, 2014).

The evaluation of these new forms of CTE should remain an active area of inquiry.

29

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32

Figures and Tables

Figure 1: Course-taking by American high school students.

Source: Statistics taken by NCES from High School Transcript Study (HSTS), 1990, 2000, 2005, and 2009.Original table available at http://nces.ed.gov/surveys/ctes/tables/h125.asp. One credit is equal to adaily year-long course.

33

Figure 2: CDF of Vocational course-taking.

(a) Share courses vocational (b) Count of vocational courses

Notes: Figures show cumulative distributions of total and share (Voc/Total) courses taken. One credit isequal to one hour per day for a full academic year. N=4,414.

34

Figure 3: Credits taken in each subject.

(a) English (b) Math (c) Science

(d) Social studies (e) Language (f) Art/Music

(g) Phys. Ed. (h) Other (i) Vocational

Notes: Figures show histograms of credits taken, defined as courses completed, even if it is an F or coursethat does not accumulate credit hours. One credit is equal to one hour per day for a full academic year.N=4,414.

35

Figure 4: Number of courses by low/high-experience, by type.

0.2

.4.6

.81

Gen. o

ther

Public

Gen. te

ch ed

.

Health

care

Educa

tion

Service

Agricu

lture

Compu

ters

Genera

l

Keybo

arding

Busine

ss

Tech

& Ind

Credits, low Credits, high/experence

Notes: Figure shows number of carnegie credits earned in each type of vocational course. “Low” courses areentry level. “High/experience” are upper-level or internship/work-based experience courses. One credit isequal to one hour per day for a full academic year. N=4,414.

36

Figure 5: Number of credits taken by subject and grade.

Notes: Students who dropout are only counted above in grades they attempted. One credit is equal to onehour per day for a full academic year. N=4,414.

37

Figure 6: Number of credits taken by subject and AFQT quartile.

Notes: Shapes mark average total number of credits taken in each subject, by AFQT quartile. One credit isequal to one hour per day for a full academic year. N=3,703 with non-missing AFQT.

38

Figure 7: Pairwise joint distributions of Core, Elective and Vocational credits accumulated for high school graduates.

Notes: Figures show scatterplots of total credits accumulated for high school graduates. Core courses include English, math, science andsocial studies. Electives include art/music, foreign languages, physical education and “other” courses. One credit is equal to a one hour perday full year course. N=4,414.

39

Table 1: Sample comparison.

Full NLSY Analysis sample Wage samplemean (sd) mean (sd) mean (sd)

DemographicsMale 0.50 (0.50) 0.48 (0.50) 0.49 (0.50)Black 0.25 (0.43) 0.22 (0.42) 0.23 (0.42)Hispanic 0.20 (0.40) 0.17 (0.37) 0.17 (0.38)Other 0.04 (0.19) 0.04 (0.20) 0.04 (0.19)Mom <HS 0.25 (0.43) 0.36 (0.48) 0.36 (0.48)Mom >HS 0.10 (0.30) 0.17 (0.38) 0.17 (0.37)Ever aid 0.28 (0.45) 0.37 (0.48) 0.39 (0.49)South age 12 0.32 (0.47) 0.31 (0.46) 0.32 (0.47)Pov. Ratio 1.49 (2.46) 2.49 (2.89) 2.42 (2.79)Public HS 0.93 (0.26) 0.92 (0.28) 0.92 (0.27)Gifted class 0.17 (0.37) 0.21 (0.41) 0.19 (0.40)Bilingual 0.03 (0.18) 0.03 (0.17) 0.03 (0.17)AFQT (Z) 0.00 (1.00) 0.00 (1.00) -0.04 (1.00)AFQT missing 0.45 (0.49) 0.16 (0.37) 0.16 (0.37)

Course-takingVoc low 1.41 (2.00) 2.28 (1.92) 2.32 (1.93)Voc high 0.54 (1.14) 0.89 (1.29) 0.91 (1.31)Core low 5.86 (5.03) 9.88 (2.65) 9.94 (2.61)Core high 2.67 (3.27) 4.87 (3.20) 4.73 (3.13)Electives 4.39 (4.08) 7.50 (2.69) 7.43 (2.69)

Attainment< HS 0.16 (0.37) 0.06 (0.23) 0.05 (0.22)HS 0.18 (0.39) 0.17 (0.38) 0.19 (0.39)Some college 0.30 (0.46) 0.34 (0.47) 0.33 (0.47)2-year degree 0.07 (0.26) 0.08 (0.28) 0.08 (0.27)4-year degree 0.28 (0.45) 0.35 (0.48) 0.34 (0.47)

Obs. (n) 8,984 4,414 3,796

Notes: Analysis sample restricts to respondents with transcripts in all years of HS. Also restricts to thosewho attended 9th grade. Coursetaking restrictions are: took at least 2 of each core course for hs grads;took 1 of each core course if 10th grade+ dropout; took between 20 and 36 credits for grads; took atleast 4 credits for dropouts. Wage sample consists of respondents who have a reported wage > $1.00/hrafter labor market entry. Ever aid indicates if respondent’s family ever received government aid. PublicHS indicates if primary high school was public. Gifted indicates if responident ever took a “gifted” class.Attainment is degree ever completed. Core credits include Eng., Math, Science, Social Studies. Electivesinclude Language, Art/Music, Phys. Ed. and Other. Low/High core indicates upper or lower level coursewithin subject-grade. Low vocational indicates entry level course. High vocational indicates second courseor higher, or an internship/experiential learning.

40

Table 2: Sample means by tercile of CTE course taking.

No CTE Lowest tercile Middle Tercile Top Tercile Allmean (sd) mean (sd) mean (sd) mean (sd) mean (sd)

A. Demographics

Male 0.33 (0.47) 0.42 (0.49) 0.49 (0.50) 0.59 (0.49) 0.48 (0.50)Black 0.17 (0.37) 0.20 (0.40) 0.24 (0.42) 0.25 (0.43) 0.22 (0.42)Hispanic 0.12 (0.33) 0.18 (0.38) 0.18 (0.39) 0.15 (0.35) 0.17 (0.37)Other 0.08 (0.27) 0.05 (0.21) 0.04 (0.20) 0.02 (0.15) 0.04 (0.20)AFQT-Z 0.65 (0.93) 0.29 (0.97) 0.06 (0.93) -0.15 (0.91) 0.11 (0.97)AFQT, missing 0.14 (0.35) 0.16 (0.37) 0.17 (0.37) 0.16 (0.37) 0.16 (0.37)Mom, <HS 0.30 (0.46) 0.33 (0.47) 0.36 (0.48) 0.40 (0.49) 0.36 (0.48)Mom, HS 0.41 (0.49) 0.47 (0.50) 0.48 (0.50) 0.48 (0.50) 0.47 (0.50)Mom, >HS 0.29 (0.45) 0.20 (0.40) 0.16 (0.37) 0.12 (0.33) 0.17 (0.38)Poverty ratio (1997) 4.82 (3.98) 3.64 (3.10) 3.29 (2.72) 2.80 (2.34) 3.36 (2.88)Ever public aid 0.33 (0.47) 0.36 (0.48) 0.40 (0.49) 0.51 (0.50) 0.41 (0.49)South, age 12 0.27 (0.45) 0.28 (0.45) 0.31 (0.46) 0.37 (0.48) 0.31 (0.46)Rural, age 12 0.09 (0.28) 0.10 (0.31) 0.12 (0.33) 0.17 (0.38) 0.13 (0.33)

B. HS characteristics

Main HS public 0.84 (0.37) 0.87 (0.34) 0.94 (0.24) 0.97 (0.16) 0.92 (0.28)Spec. Ed. 0.06 (0.23) 0.04 (0.19) 0.04 (0.20) 0.07 (0.25) 0.05 (0.21)Bilingual Ed. 0.03 (0.17) 0.03 (0.18) 0.03 (0.18) 0.02 (0.15) 0.03 (0.17)Any gifted courses 0.30 (0.46) 0.27 (0.44) 0.20 (0.40) 0.12 (0.33) 0.21 (0.41)Ever private HS 0.15 (0.36) 0.13 (0.33) 0.05 (0.22) 0.02 (0.13) 0.07 (0.26)

C. Outcomes

< HS 0.07 (0.26) 0.06 (0.24) 0.06 (0.23) 0.05 (0.21) 0.06 (0.23)HS 0.08 (0.27) 0.11 (0.32) 0.17 (0.38) 0.26 (0.44) 0.17 (0.38)Some college 0.26 (0.44) 0.31 (0.46) 0.36 (0.48) 0.36 (0.48) 0.34 (0.47)2-yr degree 0.05 (0.22) 0.08 (0.27) 0.08 (0.27) 0.10 (0.30) 0.08 (0.28)4-yr+ degree 0.54 (0.50) 0.43 (0.50) 0.34 (0.47) 0.23 (0.42) 0.35 (0.48)Last (log) wage 2.85 (0.55) 2.79 (0.48) 2.76 (0.49) 2.75 (0.49) 2.77 (0.49)

D. Course taking

Core courses, low 8.83 (2.97) 9.81 (2.88) 10.06 (2.58) 9.97 (2.27) 9.88 (2.65)Core courses, high 6.41 (3.58) 5.41 (3.42) 4.69 (3.06) 4.08 (2.70) 4.87 (3.20)Electives 9.69 (3.00) 8.49 (2.55) 7.42 (2.29) 5.87 (2.28) 7.50 (2.69)Vocational low 0.00 (0.00) 1.01 (0.58) 2.26 (0.91) 4.41 (2.09) 2.28 (1.92)Vocational, high 0.00 (0.00) 0.27 (0.42) 0.78 (0.81) 2.00 (1.78) 0.89 (1.29)

N 234 1,604 1,356 1,220 4,414

Notes: Core credits include Eng., Math, Science, Social Studies. Electives include Language, Art/Music, Phys.Ed. and Other. Low/High core indicates upper or lower level course within subject-grade. Low vocationalindicates entry level course. High vocational indicates second course or higher, or an internship/experientiallearning.

41

Table 3: Vocational courses taken, grades 10-12.

(1) (2) (3) (4) (5) (6)All Voc courses Voc Low Voc High

Male 0.599*** 0.599*** 0.559*** 0.567*** 0.349*** 0.258***(0.065) (0.064) (0.065) (0.066) (0.051) (0.037)

Black -0.200** -0.421*** -0.451*** -0.457*** -0.324*** -0.089(0.093) (0.095) (0.096) (0.099) (0.076) (0.057)

Hispanic -0.482*** -0.626*** -0.635*** -0.631*** -0.426*** -0.205***(0.094) (0.098) (0.097) (0.101) (0.080) (0.055)

Other race -0.705*** -0.743*** -0.728*** -0.718*** -0.399*** -0.332***(0.124) (0.124) (0.125) (0.129) (0.099) (0.065)

Mom < HS 0.115 0.093 0.090 0.083 0.055 0.039(0.083) (0.083) (0.082) (0.085) (0.065) (0.047)

Mom > HS -0.463*** -0.399*** -0.398*** -0.420*** -0.273*** -0.148***(0.085) (0.084) (0.083) (0.085) (0.063) (0.047)

Ever aid 0.354*** 0.276*** 0.255*** 0.319*** 0.288*** 0.011(0.078) (0.078) (0.078) (0.081) (0.061) (0.045)

South, age 12 0.342*** 0.307*** 0.325*** 0.366*** 0.330*** 0.005(0.078) (0.077) (0.078) (0.080) (0.062) (0.043)

Rural, age 12 0.241** 0.222** 0.217** 0.184* 0.201** 0.042(0.108) (0.106) (0.106) (0.108) (0.084) (0.058)

Pov. ratio -0.001*** -0.000*** -0.000*** -0.001*** -0.000*** -0.000*(0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

Main HS Public 1.118*** 1.066*** 1.058*** 1.082*** 0.725*** 0.298***(0.086) (0.086) (0.086) (0.087) (0.064) (0.048)

Gifted -0.715*** -0.461*** -0.340*** -0.357*** -0.246*** -0.103**(0.068) (0.072) (0.079) (0.081) (0.058) (0.046)

Bilingual 0.023 -0.036 -0.023 -0.036 0.184 -0.243***(0.174) (0.171) (0.171) (0.175) (0.142) (0.067)

AFQT-Z -0.384*** -0.326*** -0.347*** -0.245*** -0.072***(0.042) (0.044) (0.046) (0.035) (0.027)

Core GPA9 -0.139*** -0.154*** -0.122*** -0.016(0.038) (0.039) (0.031) (0.021)

Math 9th, hi -0.093 -0.084 -0.103 0.002(0.116) (0.118) (0.089) (0.068)

Eng 9th, hi -0.170* -0.205** -0.227*** 0.028(0.087) (0.088) (0.063) (0.050)

H.S. Grads only X X XYear 9th X X X X X X

R2 0.101 0.118 0.122 0.136 0.128 0.039Obs. (n) 4,414 4,414 4,414 4,165 4,165 4, d165

Notes: One course (dep. var) is one full year. Math/Eng. high are indicators = 1 if respondent was in upperlevel Eng./Math courses in 9th grade. Cols 4-6 are restricted to HS grads only. Core credits include Eng.,Math, Science, Social Studies. Electives include Language, Art/Music, Phys. Ed. and Other. Low/High coreindicates upper or lower level course within subject-grade. Low vocational indicates entry level course. Highvocational indicates second course or higher, or an internship/experiential learning.(∗p < 0.10, ∗ ∗ p < 0.05, ∗ ∗ ∗p < 0.01.)

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Table 4: Dep. vars. indicate 2 or 4 year college attendance or completion - conditional oncompleting high school.

Panel A: Number of credits

(1) (2) (3) (4) (5) (6)Attend 2yr Attend 4yr Attend any Earn 2yr Earn 4yr Earn any

Voc., low -0.004 -0.006 -0.010** -0.003 0.001 0.001(0.004) (0.004) (0.004) (0.008) (0.006) (0.005)

Voc., high 0.007 -0.009* -0.002 0.011 0.011 0.009(0.006) (0.005) (0.005) (0.010) (0.008) (0.007)

Core credits, low -0.013*** 0.024*** 0.011*** 0.001 0.005 0.010**(0.004) (0.004) (0.003) (0.008) (0.006) (0.005)

Core credits, high -0.022*** 0.040*** 0.018*** 0.026*** 0.026*** 0.034***(0.003) (0.004) (0.003) (0.009) (0.005) (0.004)

Elective credits -0.005* 0.018*** 0.013*** -0.006 0.010*** 0.010***(0.003) (0.003) (0.002) (0.006) (0.004) (0.003)

AFQT-Z -0.032*** 0.116*** 0.085*** 0.028 0.035** 0.053***(0.009) (0.009) (0.008) (0.019) (0.014) (0.011)

R2 0.095 0.306 0.159 0.083 0.143 0.176

Panel B: Share of credits

(1) (2) (3) (4) (5) (6)Attend 2yr Attend 4yr Attend any Earn 2yr Earn 4yr Earn any

Share Voc. low*100 0.000 -0.006*** -0.006*** 0.001 -0.002 -0.002(0.001) (0.001) (0.001) (0.002) (0.002) (0.001)

Share Voc. high*100 0.003** -0.007*** -0.004*** 0.005* -0.000 -0.000(0.001) (0.001) (0.001) (0.003) (0.002) (0.002)

Share core low*100 -0.002* 0.001 -0.001 0.002 -0.001 -0.000(0.001) (0.001) (0.001) (0.002) (0.002) (0.001)

Share core high*100 -0.005*** 0.006*** 0.001 0.008*** 0.004*** 0.007***(0.001) (0.001) (0.001) (0.003) (0.002) (0.001)

AFQT-Z -0.031*** 0.115*** 0.084*** 0.029 0.034** 0.052***(0.009) (0.009) (0.008) (0.019) (0.014) (0.011)

Total credits -0.011*** 0.021*** 0.010*** 0.002 0.011*** 0.014***(0.002) (0.002) (0.002) (0.005) (0.003) (0.003)

R2 0.096 0.307 0.159 0.083 0.144 0.177

Controls and observations apply to both tables.

If attended college X X XGPA9 X X X X X XControls X X X X X XObs. (n) 4165 4165 4165 917 2491 3408

Notes: Sample is limited to HS graduates. Core credits include Eng., Math, Science, Social Studies. Electivesinclude Language, Art/Music, Phys. Ed. and Other. Low/High core indicates upper or lower level coursewithin subject-grade. Low vocational indicates entry level course. High vocational indicates second courseor higher, or an internship/experiential learning.Controls include: gender, race, mother’s education, ruralor South at age 12, family poverty ratio in 1997, public primary high school, any gifted courses, bilingualeducation, and year entered 9th grade.(∗p < 0.10, ∗ ∗ p < 0.05, ∗ ∗ ∗p < 0.01.)

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Table 5: Dependent variable is log (real) hourly wage.

(1) (2) (3) (4) (5) (6) (7)

Voc., low -0.018*** -0.003 0.001 0.001 0.002 0.000 0.002(0.003) (0.003) (0.003) (0.003) (0.003) (0.003) (0.003)

Voc., high 0.016*** 0.018*** 0.019*** 0.019*** 0.019*** 0.016*** 0.016***(0.005) (0.005) (0.005) (0.005) (0.005) (0.005) (0.005)

AFQT-Z 0.061*** 0.050*** 0.037*** 0.039***(0.008) (0.008) (0.008) (0.008)

Core GPA9 0.028*** 0.021*** 0.010 0.010(0.006) (0.007) (0.007) (0.007)

Core credits, low 0.000 -0.002 -0.008*** -0.008**(0.003) (0.003) (0.003) (0.003)

Core credits, high 0.021*** 0.014*** 0.002 0.003(0.003) (0.003) (0.003) (0.003)

Elective credits 0.001 -0.001 -0.005** -0.006**(0.002) (0.002) (0.003) (0.003)

< High school -0.098*** -0.112***(0.025) (0.026)

Some college 0.004 0.002(0.015) (0.015)

2-year degree 0.076*** 0.095***(0.025) (0.025)

4-year degree + 0.187*** 0.198***(0.019) (0.020)

If hours>30 XControls X X X X X X

R2 0.007 0.200 0.216 0.212 0.221 0.241 0.274N 20,572 20,572 20,572 20,572 20,572 20,572 16,829n 3,796 3,796 3,796 3,796 3,796 3,796 3,594

Notes: Sample is wage sample. Wages are in real $2010. Core credits include Eng., Math, Science, SocialStudies. Electives include Language, Art/Music, Phys. Ed. and Other. Low/High core indicates upper orlower level course within subject-grade. Low vocational indicates entry level course. High vocational indi-cates second course or higher, or an internship/experiential learning.Controls include: gender, race, mother’seducation, rural or South at age 12, family poverty ratio in 1997, public primary high school, any giftedcourses, bilingual education, and year entered 9th grade.(∗p < 0.10, ∗ ∗ p < 0.05, ∗ ∗ ∗p < 0.01.)

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Table 6: Dependent variable is log hourly wage, course-taking defined in shares of total.

(1) (2) (3) (4) (5)

Share Voc. low*100 -0.004*** -0.000 0.001 0.001 0.001(0.001) (0.001) (0.001) (0.001) (0.001)

Share Voc. high*100 0.004*** 0.005*** 0.005*** 0.005*** 0.005***(0.001) (0.001) (0.001) (0.001) (0.001)

Share core low*100 -0.004*** -0.001 -0.001 -0.000 -0.001(0.001) (0.001) (0.001) (0.001) (0.001)

Share core high*100 0.005*** 0.005*** 0.004*** 0.002** 0.002**(0.001) (0.001) (0.001) (0.001) (0.001)

Total credits -0.004**(0.002)

Highest degree X XAFQT X X XGPA9 X X XControls X X X X

R2 0.047 0.213 0.223 0.242 0.243N 20572 20572 20395 20395 20395n 3796 3796 3767 3767 3767

Notes: Shares are the share of total courses taken in each subject group multiplied by 100. Share of coursesthat are electives is the omitted category. Sample is wage sample. Wages are in real $2010. Core credits includeEng., Math, Science, Social Studies. Electives include Language, Art/Music, Phys. Ed. and Other. Low/Highcore indicates upper or lower level course within subject-grade. Low vocational indicates entry level course.High vocational indicates second course or higher, or an internship/experiential learning.Controls include:gender, race, mother’s education, rural or South at age 12, family poverty ratio in 1997, public primary highschool, any gifted courses, bilingual education, and year entered 9th grade.(∗p < 0.10, ∗ ∗ p < 0.05, ∗ ∗ ∗p < 0.01.)

45

Table 7: Wage regression with labor market controls.

State FE ED distribution MSA/State FE(1) (2) (3) (4) (5) (6)

Voc., low 0.004 0.001 0.005 0.003 0.007** 0.004(0.003) (0.003) (0.003) (0.003) (0.003) (0.003)

Voc., high 0.021*** 0.017*** 0.020*** 0.017*** 0.022*** 0.018***(0.005) (0.005) (0.005) (0.005) (0.005) (0.005)

Core, low -0.001 -0.008** -0.002 -0.009*** 0.000 -0.006*(0.003) (0.003) (0.003) (0.003) (0.003) (0.003)

Core, high 0.017*** 0.004 0.013*** 0.002 0.016*** 0.005(0.003) (0.003) (0.003) (0.003) (0.003) (0.003)

Electives 0.000 -0.005* 0.000 -0.005* 0.002 -0.003(0.003) (0.003) (0.002) (0.003) (0.003) (0.003)

BA share in MSA 1.054*** 1.085***(0.263) (0.257)

AA share in MSA -4.007*** -3.539***(0.909) (0.890)

HS share in MSA 0.745** 0.892***(0.353) (0.345)

Not MSA 0.373 0.485*(0.257) (0.250)

Degree indicators X X XFixed effects State State MSA+state MSA+stateControls X X X X X X

R2 0.236 0.255 0.23 0.249 0.259 0.277N 20,198 20,198 20,172 20,172 20,172 20,172n 3,726 3,726 3,717 3,717 3,717 3,717

Notes: Wages are in real $2010. Core credits include Eng., Math, Science, Social Studies. Electives includeLanguage, Art/Music, Phys. Ed. and Other. Low/High core indicates upper or lower level course withinsubject-grade. Low vocational indicates entry level course. High vocational indicates second course or higher,or an internship/experiential learning.Controls include: gender, race, mother’s education, rural or South atage 12, family poverty ratio in 1997, public primary high school, any gifted courses, bilingual education, andyear entered 9th grade.(∗p < 0.10, ∗ ∗ p < 0.05, ∗ ∗ ∗p < 0.01.)

46

Table 8: Wage Regressions by Type and Education Needs of Local Labor Market.

c

By metro area type By labor market education needsAll Not MSA MSA P/CMSA No FE No FE State FE MSA FE(1) (2) (3) (4) (5) (6) (7) (8)

Voc low 0.002 0.007 0.006 0.000 0.002 0.024 0.036 0.033(0.003) (0.006) (0.005) (0.007) (0.004) (0.024) (0.024) (0.024)

X BA share in MSA -0.091 -0.135 -0.116(0.098) (0.100) (0.097)

Voc high/exper 0.019*** 0.006 0.026*** 0.022*** 0.025*** -0.007 -0.019 -0.017(0.005) (0.007) (0.008) (0.008) (0.006) (0.035) (0.034) (0.033)

X BA share in MSA 0.133 0.185 0.177(0.145) (0.142) (0.139)

BA share in MSA 0.992*** 1.069*** 1.292***(0.160) (0.280) (0.325)

Fixed effects State FE MSA FEControls X X X X X X X X

R2 0.22 0.232 0.227 0.200 0.224 0.225 0.239 0.258N 20,572 4,470 9,310 6,392 15,702 15,702 15,702 15,702n 3,796 804 1,686 1,227 2,913 2,913 2,913 2,913

Notes: Wages are in real $2010. Core credits include Eng., Math, Science, Social Studies. Electives include Language, Art/Music, Phys.Ed. and Other. Low/High core indicates upper or lower level course within subject-grade. Low vocational indicates entry level course. Highvocational indicates second course or higher, or an internship/experiential learning.(∗p < 0.10, ∗ ∗ p < 0.05, ∗ ∗ ∗p < 0.01.)

47

Table 9: Dependent variable is log (real) hourly wage; regressions by highest degree completedand gender.

Panel A: Female

(1) (2) (3) (4) (5)No HS HS Some coll 2-yr 4-yr+

Voc low -0.011 -0.000 -0.009 -0.035** -0.003(0.017) (0.009) (0.007) (0.016) (0.009)

Voc high/exper. 0.036 0.007 0.014 0.032 0.006(0.024) (0.012) (0.010) (0.024) (0.015)

Core credits, low -0.000 0.010 -0.011* -0.019 -0.002(0.008) (0.009) (0.006) (0.018) (0.008)

Core credits, high -0.011 0.025** -0.003 -0.014 0.006(0.013) (0.011) (0.006) (0.017) (0.007)

Elective credits -0.002 -0.001 -0.004 -0.013 -0.012**(0.012) (0.007) (0.006) (0.013) (0.006)

AFQT Yes Yes Yes Yes YesControls Yes Yes Yes Yes Yes

R2 0.194 0.141 0.162 0.240 0.176N 596 2072 3752 889 3083n 91 305 622 170 746

Panel B: Male

(1) (2) (3) (4) (5)No HS HS Some coll 2-yr 4-yr+

Voc low -0.002 -0.005 0.008 0.004 0.031**(0.013) (0.007) (0.007) (0.021) (0.013)

Voc high/exper. 0.015 0.012 0.033*** 0.052** 0.004(0.021) (0.011) (0.010) (0.020) (0.014)

Core credits, low -0.001 -0.013 -0.013** -0.011 -0.008(0.013) (0.009) (0.006) (0.017) (0.011)

Core credits, high -0.002 -0.009 -0.001 -0.019 0.016(0.023) (0.010) (0.007) (0.021) (0.010)

Elective credits 0.031** -0.009 0.000 -0.007 -0.013(0.015) (0.007) (0.006) (0.014) (0.008)

AFQT Yes Yes Yes Yes YesControls Yes Yes Yes Yes Yes

R2 0.270 0.176 0.168 0.261 0.184N 705 2901 3653 684 2237n 111 422 646 140 543

Notes: Wages are in real $2010. Core credits include Eng., Math, Science, Social Studies. Electives includeLanguage, Art/Music, Phys. Ed. and Other. Low/High core indicates upper or lower level course withinsubject-grade. Low vocational indicates entry level course. High vocational indicates second course or higher,or an internship/experiential learning.(∗p < 0.10, ∗ ∗ p < 0.05, ∗ ∗ ∗p < 0.01.)

48

Table 10: Dependent variable is log (real) hourly wage.

(1) (2) Mean voc. credits earned

Tech. & Industry 0.016*** 0.014*** 1.03(0.004) (0.004)

GLMP Tech. Ed. -0.019 -0.022 0.90(0.014) (0.014)

Business/Management 0.011** 0.007 0.64(0.005) (0.005)

GLMP Keyboarding 0.006 0.000 0.35(0.012) (0.012)

GLMP General -0.005 -0.007 0.30(0.007) (0.007)

Computer Tech. 0.004 0.001 0.30(0.009) (0.009)

Agriculture 0.012 0.011 0.19(0.008) (0.008)

Service -0.002 -0.004 0.17(0.005) (0.005)

Edu. & Child Care -0.012 -0.014 0.12(0.010) (0.009)

Health care 0.016 0.015* 0.09(0.010) (0.009)

Pub. & Protect. Svcs. -0.013 -0.017 0.04(0.011) (0.011)

GLMP Ind. Arts -0.059** -0.059** 0.02(0.024) (0.024)

GLMP Other -0.025* -0.024** 0.01(0.014) (0.012)

Core credits, low -0.003 -0.010***(0.003) (0.003)

Core credits, high 0.013*** 0.002(0.003) (0.003)

Elective credits -0.002 -0.006**(0.003) (0.003)

Highest degree XAFQT X XAll controls X X

R2 0.223 0.243N 20,495 20,495n 3,786 3,786 3,786

Notes: Wages are in real $2010.(∗p < 0.10, ∗ ∗ p < 0.05, ∗ ∗ ∗p < 0.01.)

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Table A1: Vocational course types in the NLSY97.

1. Basic Keyboarding/Typewriting (GLMP).2. Industrial Arts (GLMP).3. Career Preparation/General Work Experience (GLMP).4. Technology Education (GLMP).5. Other (GLMP).6. Agriculture and Renewable Resources.7. Business:

Business Services; Marketing and Distribution; Business Management.8. Health Care.9. Public and Protective Services.10. Transportation and Industry:

Construction trades; Material Moving; Mechanics and repairPrecision Production (Drafting/Graphics/Printing/Metals/Wood/Plastics).

11. Computers and technology:Computer Technology, Communication Technology; Other technology

12. Service:Personal and Other Services; Food Service and Hospitality

13. Child Care and EducationNotes: GLMP is General Labor Market Preparation, as opposed to specific “occupationaleducation” courses.

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